Mystery Rays from Outer Space

Meddling with things mankind is not meant to understand. Also, pictures of my kids

July 30th, 2007

Artificial immune systems

The July issue of Nature Reviews Immunology has an intriguingly-titled opinion piece by I.R. Cohen, “Real and artificial immune systems: computing the state of the body.” 1 Maybe I’m missing something, but I find it quite disappointing. Part of that is probably that I’m not exactly the audience he’s after, but I think that’s not all of it.

The paper starts off in a humble and apologetic tone with the explanation that “I attempt to show that reframing our view of the immune system in computational terms is worth our while”. To me it’s self-evident that framing the immune system in this way could be worthwhile (so long as we don’t abandon other frames, of course). I can easily imagine that there are lots of immunologists out there who are skeptical of it, though, and presumably those are the intended audience for the introduction.

Organic computer (Woody Igou)Most of the first half of the paper seems to be a pretty basic introduction to some computational concepts — they’re concepts I’m familiar with, which means they must be basic. A couple of mildly interesting comments arise here, particularly the notion that “the immune system effectively computes the immunogenic state of the body”. I don’t think this is a deeply profound thought, just a re-framing (as Cohen says — I’m not criticizing here, that’s what he said he was doing) of an overview of the immune system. Another interesting point is Cohen’s contrast of the immune system and a Turing system, in that the former is self-organizing: “It may therefore be said that the immune system creates and modifies its own program as it goes”.

At this point I was thinking that these were some interesting turns of phrase, but was wondering whether it was just semantics. A new approach to a field is interesting as far as it generates new questions or answers that can be tested experimentally, so I was looking, in the second half of the paper, for some examples of this; at least some of the kinds of questions that could be addressed. This was where I was really disappointed. The examples he offers as powerful outcomes of “reframing immune-system behavior in computational terms” don’t seem to me to be particularly powerful, or particularly dependent on the reframing.

He talks about “Natural immune reactivity to self-antigens” as one example, and “Assessing states of stress” as another. He claims that “The computational view of the immune system sees natural autoimmunity as a physiological mechanism for detecting and responsing to the states of body cells and tissues”, and contrasts this to the “mainstream” view which has “little tolerance … for the idea that natural autoimmunity could serve some useful purpose”.

First, at least as I remember it, this notion of autoimmunity as functional is one that has repeatedly popped up throughout the history of immunology (so it’s not something that computational immunology has a unique handle on), and the reason it’s not widely accepted is not that it’s been rejected mindlessly, but that there’s never been much or good evidence put forward for it. Rephrasing an old idea in the sparkly new wrapping du jour isn’t helpful unless it offers a new way to test it — which I don’t see here.

The other problem is that this seems to come out of the blue. He claims that the computational view sees autoimmunity in this way but doesn’t really show the connections; to be honest it comes across to me as someone with a particular hobbyhorse, trying to drum up support for an old idea. In other words, I’m neither convinced that this concept is either new to computational immunology, nor that it’s particularly strongly suggested by computational immunology. To be fair, this is a short section in a short review paper, and in a longer treatment I might be more convinced by the argument.

I have the same concerns about the “states of stress” argument, though I will say that this basic concept is one I find much more plausible — though again I’m not convinced it’s particularly unique to this approach, or that it’s particularly strongly suggested by this approach.

So I’m a sympathetic audience to the basic concept that a computational approach to immunology could have some useful outcomes, but I’m not blown away by the examples in this paper. I’d like to see more explanation of the kinds of questions and answers this approach could provide, with concrete examples.

  1. Real and artificial immune systems: computing the state of the body. Cohen IR. Nat Rev Immunol. 2007 Jul;7(7):569-74. []
July 26th, 2007

Immune evasion: What is it good for?

When a virus infects something, it has to overcome a wide range of antiviral responses. In mammals, those responses include innate responses like those mediated by toll-like receptors; natural killer cells; apoptosis; cytokines; and specific immune responses including B cells (antibodies), T helper cells, and cytotoxic T lymphocytes.

Any successful virus has to be able to deal with those defenses, and especially in the larger viruses such as adenoviruses, poxviruses, and herpesviruses (which have the extra room in their genomes) there can be a dizzying number of immune evasion molecules. Many of these viruses encode defenses against cytotoxic T lymphocytes (CTL, or CD8+ T cells), in the form of molecules that target class I major histocompatibility complexes. (MHC class I is the receptor that CTL recognize at the target cell surface.) Such molecules are nearly universal among herpesviruses, and are common in adeno- and poxviruses. These immune evasion molecules are clearly critical to the virus’s pathogenicity.

Any time you find yourself saying “clearly” you should beware, because it usually means it’s something that hasn’t been tested. In the case of viral immune evasion molecules, their in vivo role has rarely been looked at, at least partly because many of the molecules have been identified in human herpesviruses, which are highly species-specific; you won’t find many volunteers lining up to be tested with a mutant herpes simplex virus to see if it’s more pathogenic.

But there are a couple of mouse herpesviruses that have immune evasion genes, so the experiment can be done. You’d predict that a mouse herpesvirus lacking its immune evasion genes would be less pathogenic (because the T cell response would be more efective), and you’d expect that in the wild-type virus that contains its immune evasion genes (compared to the mutant virus) the CTL response would be smaller and weaker. Ann Hill has been testing this concept, and her findings are pretty disconcerting.

MCMVSo far there’s a series of three papers in which Ann looks at the effect on antiviral immunity when murine cytomegalovirus (image on right from the Oxenius lab) is stripped of its three known anti-CTL immune evasion molecules. In the first paper,1 the findings were simple: There was essentially no effect.

During acute infection, there was very little difference between the two viruses2 with respect to the kinetics of viral replication and clearance, or in the size and kinetics of the virus-specific CD8 T cell response. During chronic infection … the size and phenotype of the CD8 T cell response to both viruses was remarkably similar. … Thus, restoring the ability of CD8 T cells to detect MCMV had little apparent effect on the course of MCMV infection and on the CD8 T cell response to it.

A little later they did finally turn up an effect of the immune evasion molecules: Viruses lacking them didn’t replicate as well in the salivary glands.3 That may sound like a trivial difference (just one anatomic site? — and the difference wasn’t absolute, merely a reduction by about ten-fold) but it may be important for virus spread between mice. Still, especially after the most recent paper, which shows that even the pattern of T cell recognition (immunodominance) is not affected by CTL immune evasion,4 we’re left with a picture in which viral evasion of CTL hardly seems worth the trouble

Overall, the data indicate that the presence or absence of MHC I immune evasion genes has remarkably little impact on the size or specificity of the MCMV-specific CD8 T cell response over an entire lifetime of infection.

So what’s going on here? Obviously I don’t know any more than anyone else, but here are some of my thoughts:
(1) Is there some problem with the experiments? One concern I have is that these were done in C57BL/6 mice, which are known to be highly resistant to MCMV through NK cells (i.e. before CTL even get involved), and this resistance is unusual among mice.5 Maybe in other strains, evasion of CTL would be more important. The problem here is that even if it’s true, Ann looked specifically at CTL responses and didn’t find that they were much changed by the immune evasion molecules. Even if CTL are not important the immune evasion molecules are apparently targeting them.

(2) Maybe CTL are not as important for immunity as we might think. In general mice and humans lacking CD8+ T cells or components of the MHC class I antigen presentation pathway do surprisingly well, immunologically. They can generally deal with viral infections reasonably well (though not as well as intact individuals). Maybe there’s just so much redundancy that CTL can be skipped without altering overall immnunity much. Doesn’t get us over the problem that there was no difference in the CTL response specifically.

(3) Maybe immune evasion genes are not all that important. Perhaps they confer no more than a small replication advantage to the viruses. But then why are they so univeral among these viruses? Given the lack of effect that Ann sees in MCMV, you’d expect to turn up, say, clinical isolates of human cytomegalovirus that have dropped some of their immune evasion genes. But as far as I know there are no such examples; the immune evasion genes are tightly conserved.

(4) Maybe the immune evasion genes are only important under specific circumstances, that weren’t present in these laboratory-housed mice. Mouse to mouse spread, perhaps. That seems to be the most attractive idea so far, given the apparent effect in salivary glands only. Or perhaps these genes kick in when the mice are concurrently infected, or there’s some effect of stress that would be more important in wild mice. Hand waving, and hard to test.

(5) Maybe there’s something unusual about MCMV. Maybe other viruses would show a much more dramatic effect if their immune evasion genes were knocked out. Not likely to be tested with the human herpesviruses, as I say, but murine herpesvirus 68 is a useful model that could be tested (MHV68 uses the mK3 imune evasion gene to block MHC class I epxression, but as far as I know a knockout viruse hasn’t been tested in vivo.)

  1. Murine Cytomegalovirus Interference with Antigen Presentation Has Little Effect on the Size or the Effector Memory Phenotype of the CD8 T Cell Response. Marielle C. Gold, Michael W. Munks, Markus Wagner, Christopher W. McMahon, Ann Kelly, Daniel G. Kavanagh, Mark K. Slifka, Ulrich H. Koszinowski, David H. Raulet and Ann B. Hill. The Journal of Immunology, 2004, 172: 6944-6953[]
  2. wild-type vs. immune-evasion-depleted virus[]
  3. Murine Cytomegalovirus Interference with Antigen Presentation Contributes to the Inability of CD8 T Cells To Control Virus in the Salivary Gland. Xiuju Lu, Amelia K. Pinto, Ann M. Kelly, Kathy S. Cho, and Ann B. Hill. J Virol 2006 80(8):4200-4202[]
  4. Viral interference with antigen presentation does not alter acute or chronic CD8 T cell immunodominance in murine cytomegalovirus infection. Munks MW, Pinto AK, Doom CM, Hill AB. J Immunol. 2007 Jun 1;178(11):7235-41.[]
  5. NK gene complex haplotype variability and host resistance alleles to murine cytomegalovirus in wild mouse populations. Anthony A Scalzo, Mitali Manzur, Catherine A Forbes, Michael G Brown and Geoffrey R Shellam. Immunology and Cell Biology (2005) 83, 144–149[]
July 23rd, 2007

Classic paper: Patterns in a haystack

In 1990, it was well known that major histocompatibility complexes bind peptides, and the structural basis for that binding was also clear; for example, Bjorkman et al’s crystal structure of HLA-A2, in 1987, showed the groove at the “top” of the MHC class I complex where peptides bind, and even showed an unstructured mass within it. A number of MHC-binding peptides had been identified, but (at least as I remember it) there was no general sense of a pattern among these peptides; there seemed to be little connecting them. Attempts to predict T cell epitopes focused more on peptide secondary structure1 or missed the point entirely by pooling together peptides from multiple different alleles.2 In some ways it was a confusing period; people were looking for binding peptides using synthetic long (say, 11mer or 15mer) peptides as they still do today, but with no guidance from patterns it was very difficult to identify the actual binding sequence (say, an 8- or 9mer) within the synthetic peptides.3 Accordingly, many of the peptides that were claimed to be epitopes, were actually too long, extended past the edges of the authentic peptide. It was a circular problem: Without knowing the authentic epitopes, you couldn’t easily find the motifs, but without knowing the motifs, it was hard to identify the authentic epitopes.

In 1990 and 1991, Hans-Georg Rammensee’s group solved this problem almost single-handedly. Their work came out in several papers, but probably the most important was:

Allele-Specific Motifs Revealed By Sequencing Of Self-Peptides Eluted From MHC Molecules
Falk K, Rotzschke O, Stevanovic S, Jung G, Rammensee HG
Nature 351 (6324): 290-296 May 23 1991

This paper is partly a methodological advance (and its methods are probably the main reason it’s been cited 1768 times, as I write this), but it gave some important insights into antigen presentation as well. More importantly (this is my blog, so this is all about me, me me) I found it a delight to read, when it came out in the early years of my PhD; it seemed such a daring approach, trying something that I would have thought (at the time) had no chance of ever working; it’s a beautiful example of pulling a simple, clear, and mostly-true model out of a haystack of data; and it helped visualize the system so clearly.

Rammensee’s first breakthrough was to directly identify an authentic MHC class I epitope.4 They did this by inventing a technique that became standard, a combination of biochemistry (to purify peptides from influenza-infected cells) and T cells (to identify the stimulating peptide). The surprise at the time was that the peptide that best stimulated the T cells did not co-purify with the peptide that had been previously identified as the influenza epitope, but rather was a shorter version:

Incidentally, both crude synthetic peptide preparations … contains other peptides of smaller size, which coeluted exactly with the respective natural peptide … The natural Db-restricted peptide coeluted with ASNENMETM … which is recognized 1,000 times better than IASNENMETMESSTLE. … The data also indicate that the use of synthetic peptides to identify T-cell epitopes may be misleading, as very minor byproducts may be responsible for much of the biologic effect.

This was also a very exciting paper in that it showed just how extraordinarily sensitive T cells are — thousands of times more sensitive than had been thought, because they weren’t recognizing the abundant synthetic peptide itself but rather the tiny amounts of contaminants in the peptide preps.

Wiley peptidesCells, even when uniformly infected with a virus, don’t present a single peptide; they present tens of thousands of different peptides, so the purification approach they had used previously was impossible for looking at overall peptide composition. 5 This is where Rammensee’s group took their bold leap forward. They were pretty confident now that the peptides associated with MHC class I had a constant, defined length of 9 amino acids, and they were pretty confident that peptides bound to a particular MHC class I allele would have some features in common — a motif for binding. So rather than try to pull out individual peptides from the whole messy gamisch on the cells, they grabbed the entire pool, all the peptides bound to one MHC class I allele, and sequenced the whole damn thing, the whole ten-thousand-peptide pool, by mass spec.

Sometimes after a breakthrough technique is published, everyone slaps their forehead and says “D’OH!”, because in hindsight it’s obvious that it should work. (PCR, for example.) This is not one of those cases. It’s still amazing to me that it works, and especially that it worked so well back in 1991 (the technique is still tricky even with today’s mass spec technology). But work it did. They pulled apart the peptides, amino acid by amino acid, and analyzed each position. (They were even able to completely sequence one specific very abundant peptide, the self-peptide SYFPEITHI.) What they saw was that, first, after 9 cycles there was little signal, consistent with their fundamental idea that the MHC class I allele (H-2Kd, in this case; they looked at several other alleles as well) bound 9mers and supporting the idea that they were really looking at authentic MHC class I-bound peptides. The other, and critical, finding was that at some positions, some amino acids were over-represented: “The Kd-eluted peptides have a distinct amino-acid residue pattern for each position from 1 to 9, whereas the mock-eluted material shows a uniform pattern of residues throughout.” At position 2 (for example), tyrosine was some 40 times as abundant as most of the other amino acids. In contrast, at other positions (position 1 and 3, for example), there was little if any difference between the amino acids. This led them to to concept of “anchor positions”, positions that tie down the whole peptide into the MHC class I binding groove. (See the picture to the right, taken from a 1993 paper by Don Wiley. It shows four different peptides that all bind HLA-A2; the side chains at each amino acid poke out fairly randomly, except for the second and the last amino acids (P2 and P9), the anchor positions, which are consistently tucked into the here-invisible pockets within the peptide-binding groove of HLA-A2.)

They were then able to take previously-identified MHC class I epitopes and neatly line them up, matching them to the anchor residues’ motifs. Abruptly, an incoherent mass of chaotic data fell into a neat, organized, and obvious pattern. And just to round out the elegance, this all fit beautifully with the MHC class I crystal structure that had been determined a few years before:

Co-crystallizing material not from the A2 sequence and bound to the cleft showed extensions (possible Leu and Val side chains) fitting the A2 pockets … Therefore, different MHC class I alleles differ in the location and shape of pockets in the cleft likely the be able specifically to accommodate certain amino-acid side chains.

After this paper, the whole epitope identification problem became much, much easier. People who had been scratching their heads over long sequences sat down with a piece of paper and found the real epitope that had been hiding in their peptide.6 Now there are thousands of well-defined perfect T cell epitopes, their sequences available in public databases — the father of which is the SYFPEITHI database, named after the self-peptide sequenced in this paper.

  1. For example, Spouge, J. L., H. R. Guy, J. L. Cornette, H. Margalit, K. Cease, J. A. Berzofsky, and C. DeLisi. 1987. Strong conformational propensities enhance T cell antigenicity. J. Immunol. 138:204-212, and DeLisi, C., and J. A. Berzofsky. 1985. T-cell antigenic sites tend to be amphipathic structures. Proc. Natl. Acad. Sci. USA 82:7048-7052.[]
  2. Rothbard, J. B., and W. R. Taylor. 1988. A sequence pattern common to T cell epitopes. EMBO J. 7:93-100.[]
  3. For example, in J Virol 65:1177-1186 (1991), a paper published by my PhD lab just as I joined them, they found the 11mer sequence TSSIEFARLQF but weren’t able to narrow it down to the actual binding peptide SSIEFARL (later identified in Virology. 1993 Jul;195(1):62-70.) []
  4. Rotzschke, O., K. Falk, K. Deres, H. Schild, M. Norda, J. Metzger, G. Jung, H. G. Rammensee. 1990. Isolation and analysis of naturally processed viral peptides as recognized by cytotoxic T cells. Nature 348: 252-254. []
  5. Also, of course, you need specific T cells for the identification step after purification, and normal self-peptides pretty much by definition don’t trigger a T cell response, so you have no readout for most of the peptides on a cell.[]
  6. It’s worth emphasizing, though, that motifs are far from perfect predictors. A significant minority of good epitopes do not match the defined motif — for some examples, see Kottori et al, which I discussed here. But most do.[]
July 22nd, 2007

Is there tenure?

Willy n' Ethel     

From Willy ‘n Ethel  for 7-22-07, by Joe Martin

July 20th, 2007

Herpesvirus microRNAs (Predictions, successful and unsuccessful)


Just yesterday I said (about the herpesvirus-encoded microRNA stories at the International Herpesvirus Workshop) that I would wait until publication before I talked about them. I know people were on tenterhooks waiting for the update, so fortunately it only took a day before something was published about them. (Image at right: Herpes simplex capsid, from this paper.)

One thing I didn’t mention about the microRNAs was how hard it seems to be to assign functions to them. Now, I’m not a microRNA expert by any means; I use siRNAs quite a bit, and I had (without thinking much about it) assumed that to find the function of a microRNA you simply look for the complementary sequence in the databases, and Bob’s your uncle. Of course, I knew that microRNAs differ from siRNAs in that they are not necessarily perfect matches, but I didn’t take the obvious next mental step and conclude that simply looking for a complementary sequence wouldn’t work.

The two main goals described in the IHW posters were to, first, identify microRNAs in herpesviral genomes. (As I said, there seem to be a bunch, up to 18 in some genomes.) But having identified them, how to find their targets? A fair bit of bioinformatic energy was turned to that aim, with a bunch of targets being identified. Many were at the preliminary stage of testing and confirmation, so it wasn’t at all clear to me how accurate these predictions are.

In today’s issue of Science, there was this article:

Host Immune System Gene Targeting by a Viral miRNA

Noam Stern-Ginossar, Naama Elefant, Albert Zimmermann, Dana G. Wolf, Nivin Saleh, Moshe Biton, Elad Horwitz, Zafnat Prokocimer1 Mark Prichard, Gabriele Hahn, Debra Goldman-Wohl, Caryn Greenfield, Simcha Yagel, Hartmut Hengel, Yael Altuvia, Hanah Margalit, Ofer Mandelboim

Science 20 July 2007:Vol. 317. no. 5836, pp. 376 – 381 DOI: 10.1126/science.1140956


This story was not reported at the Workshop, but what they find is that one of the microRNAs encoded by human cytomegalovirus (hcmv-miR-UL112) targets a member of the non-classical major histocompatibility complex family, MICB. MICB (picture at left) is a ligand for NK cells, and the conclusion is that this microRNA is an NK cell immune evasion molecule — it downregulates one of the molecules that NK cells use to recognize virus-infected cells. (This also ties in with my earlier post, where I commented on how NK cell immune evasion was flourishing these days, as we understand more about NK cell receptors.)

OK, what’s really interesting to me is this: This same microRNA was reported at the Workshop to have an entirely different target!1 At the Workshop, a different group reported that this microRNA targets some of the virus’s own regulatory proteins, and proposed that it’s involved in control of the virus’s genes.

The published paper uses “our newly developed target prediction algorithm, RepTar” — described in some detail in the supplementary information, but (I think) not publically accessible as such for testing; the abstract used a “bioinformatic approach” that may have been described in more detail on their poster, but which I don’t have here. In any case, it seems that there’s no widely-accepted technique to find valid microRNA targets.

Are both right? Both wrong? I believe there are examples of microRNAs that regulate multiple genes, but I thought they were mostly in gene families, so that it makes (teleological) sense to control them together. These are completely different genes with completely different functions. Viruses in general are very good at multi-tasking proteins (e.g. adenovirus E1B protein, which basically controls everything in an infected cell, washes dishes, and changes your oil too), so maybe they’re also good at multi-tasking microRNAs? Both groups have validated their results with other techniques, but I’d like to see third-party validation before I firmly accept either conclusion.

The most likely explanations, I think, are that (1) One of the groups is mistaken in their target identification, or that (2) I’ve mixed up two different miRNAs. I’ll have to wait for the other group’s publication to find out, if then.

  1. At least, I think it’s the same microRNA — the nomenclature is a little different, and the abstract from the workshop doesn’t offer a sequence I can use to confirm. At any rate, the Workshop version is called “UL-112-1” and published version is “hcmv-miR-UL112”; I think they’re the same.[]
July 19th, 2007

Home from the sea

Back from the International Herpesvirus Workshop, and from a mini-vacation in Statesboro, GA, where I managed to get my worst sunburn since I was a kid.1

This was the first Workshop I’ve been to since 1994 (13 years!), and I was alternately surprised by how much the field has changed, and how little it’s changed. Lots of the abstract titles could have been recycled from 1994, and we’re only a year or so away from figuring out how the latency-associated transcripts of herpes simplex work, just as we were a year or so away from figuring them out 16 years ago at my first IHW.

MicroRNAs seemed to be a big theme this year, as various herpesviruses were reported to have from 2 to 18(!) microRNAs encoded in their genomes, not to mention how they screw with the host microRNA complement. For some of the viruses, the presence of microRNAs have have already been published, but the numbers may be significantly larger than previously thought. I don’t know what the etiquette of blogging about conference proceedings and abstracts is. I think I’ll pass: Even though there were some cool things there, I’ll wait until they’re officially published. Anyway, there are lots of other things I can talk about. I see Suicyte has already mentioned one of the papers I was going to comment on, so I’ll just say that

Sequential E2s Drive Polyubiquitin Chain Assembly on APC Targets

Monica C. Rodrigo-Brenni and David O. Morgan

Cell, Vol 130, 127-139, 13 July 2007

is a very cool paper.

More when I dig my way out of two weeks’ worth of paperwork.

  1. Though compared to some of the truly epic sunburns of my childhood, this was just a minor and localized annoyance[]
July 6th, 2007

Herpes: It’s Latin for “Fun! Fun! Fun!”

I’m off for a couple of weeks. Today I’m heading to the 32nd International Herpesvirus Workshop in Asheville, NC. Depending on time, internet access, and interest, I may blog a bit from there, or I may not. After that I’ll spend a few days with my wife and kids in Savannah, GA, and then back home on the 18th or so.

July 5th, 2007

Peptide loading

PeptideNo peptideWhen T cells recognize virus-infected cells, what they’re actually “recognizing” is a short peptide that’s stuck in a class I major histocompatibility complex. The peptide sits neatly in a groove formed by two helices on top of a beta-sheet (“a hot dog in a bun”, students are told in immunology class, though to me it doesn’t look much like that).1 On the left, there’s a diagram of this groove with no peptide associated — on the right, with its peptide tucked in — and below, there’s a space-filling view (from a different angle, but till looking “down” at the MHC surface, the way a T cell would be “looking”). In this last view I’ve made the MHC atoms outline; the peptide is in brown, so you can see how tightly packed the peptide is. The “groove” is a pocket as much as a groove, and the peptide is buried fairly deeply within that pocket, with only its top surface exposed for T cells to look at. KbSIEFARL

How does the peptide wiggle into that slot? You can imagine that it would have some trouble just clicking in, like a Lego (TM) piece. What probably happens normally is that the pocket in the MHC is held in a more open configuration (the MHC class I is partially but not completely folded) until the peptide starts to settle in, and then the MHC actually finishes folding around the peptide. (There’s only circumstantial evidence for this, but it’s always hard to look at folding intermediates directly, even when they’re relatively long-lasting.) You’d expect that an accessory molecule, or molecules, might be involved either in the “holding open” phase, or in the “folding around the peptide” phase, or both.

As it happens, MHC class I interacts with a bunch of proteins during its maturation in the ER — classical chaperones like BiP, calnexin, calreticulin, ERp57, and PDI, ambiguous chaperones like tapasin, and the peptide transporter TAP. Of this list, tapasin has been the strongest candidate for “holding open” MHC class I and keeping it in a peptide-receptive state, and the finding that tapasin and ERp57 interact offered some conceptual models for how this might work.The latest issue of Nature Immunology has a paper that clarifies this:
Selective loading of high-affinity peptides onto major histocompatibility complex class I molecules by the tapasin-ERp57 heterodimer
Pamela A Wearsch & Peter Cresswell
Nature Immunology (Advance Online Publication: doi:10.1038/ni1485)

Cresswell’s group has finally managed to reconstitute peptide loading of MHC class I in vitro. There have been lots of attempts at this, but none have worked well. 2 The key turns out to be that tapasin alone doesn’t work; you need to include a tapasin-ERp57 disulphide-linked heterodimer. (Calreticulin was also part of the complex they used, though it’s not clear to me whether that was essential for loading.)Under these conditions, tapasin/ERp57 acts as a “peptide editor”, in that when tapasin/ERp57 is present low-affinity peptides are less able to compete for binding — in other words, tapasin/ERp57 helps assure that the peptides associated with MHC class I are “good” ones. Apparently the tapasin/ERp57 heterodimer directly competes with peptides for binding: 3

This observation suggests that the mechanism underlying peptide editing involves a reiterative process in which peptide displaces conjugate and conjugate displaces peptide until a sufficiently high affinity is reached that peptide remains associated.

This seems remarkably similar to the function of HLA-DM in MHC class I peptide assembly.

  1. 2007 is, I believe, the 20th anniversary of the first MHC class I crystal structure, and I’ll spend more time going over some of the more exciting features in a later post.[]
  2. Peptide will associate with a purified MHC class I/beta-2-microglobulin complex in the test tube, but it’s a very slow process, hours to days, compared to the 10-30 minutes that it takes in vivo.[]
  3. I don’t think this implies they necessarily bind to the same site, though.[]
July 2nd, 2007

Dogma supported: Antiviral immunity

It’s been dogma for a long time that cytotoxic T lymphocytes (those that express the CD8 surface marker) are important in anti-viral immunity. It’s also been dogma for a long time that memory responses are important for anti-viral immunity. Therefore, it’s dogma that memory CTL responses are important for anti-viral immunity. 1

However, the hard data that support this dogma have been hard to come by. Perpetual gadfly Rolf Zinkernagel has challenged this concept, most recently and aggressively in Immunological Reviews 211:310-319 (June 2006)2 Zinkernagel’s claim is that

While most immunologists accept a special ‘remembering’ memory quality, we have argued previously and document here that increased resistance against re-infection, i.e. immunity, reflects low-level antigen-driven T- and B-cell responses, resulting in elevated serum or mucosal titers of protective antibodies or of activated T cells, respectively. Periodic antigen re-exposure is from within, by persisting infection (long-term) or by immune complexes (short-term), or from without, by low-level re-infections.

I will say that Zinkernagel’s arguments aren’t widely accepted among the immunological community, but it’s always good to have someone question accepted wisdom; often it leads to new findings. Contrary to the usual journalistic stereotype, though, dogma almost always wins; most people who “challenge dogma” and “overturn paradigms” are flat wrong, and “one voice battling against mainstream science” is usually just one voice because they’re wrong. A recent paper strongly suggests that, as usual, this lone voice in the wilderness is wrong.

The paper is

Memory CD8 T cells are gatekeepers of the lymph node draining the site of viral infection

Ren-Huan Xu, Min Fang, Andres Klein-Szanto, and Luis J. Sigal

PNAS 104:10992-10997 (June 26, 2007)


The Sigal lab3 used ectromelia virus, mousepox, as their model. Ectromelia is an absolutely brutal virus for mice (photo of a mouse with ectromelia from a poxvirus review by Grant McFadden)4 Think smallpox for mice: High death rate, contagious, very nasty. Few researchers work with it, because it can wipe out an entire mouse colony if it gets out of hand, but it’s a terrific model for a genuine pathogen that’s controlled by the immune system. (It’s also a good model for things like smallpox, which is still of some interest for humans.)

Ectromelia starts off with a localized infection in the foot, entering through cuts and scratches. After replicating there for a while, it spreads through the rest of the body, probably through the lymphatic system. It’s in this stage that it kills the mice; spleens and livers are essentially destroyed. A handful survive this stage, and go on to recover, after a stage of skin infection. Immunity to ectromelia, as with most pox viruses, is very good. Survivors are highly resistant, and (again common among poxviruses) infection with related viruses, such as vaccinia virus, confers protection.

They came up with a number of interesting findings. I’ll summarize and will skim over the actual data.

  1. Memory did protect against infection. The mice needed both CD8 T cells and antibody for protection, though.

    These results support a model in which preexisting antibodies or memory CD8 T cells alone cannot provide sterilizing immunity but can prevent disease by delaying spread or replication of the virus long enough to give other arms of the immune system the necessary time to respond. These results are disappointing given the current interest in designing vaccines to protect immunodeficient individuals.

  2. There was no indication that persistent infection was needed to maintain the memory response.

    Of note, the recipient mice did not seroconvert during the parking period5 indicating that VACV had not been transferred with the CD8 T cells.

  3. The immunity had no effect whatsoever on the virus in the footpad, the initial site of infection. Virus levels were the same in this site whether or not the mouse had memory cells present. Perhaps this is a general effect:

    This failure of memory CD8 T cells to provide good protection at the site of entry may be general because they have been shown to protect the lung but only moderately in the case of influenza virus infection and may indicate that memory CD8 T cells may not be able to prevent clinical disease in nonsystemic viral infections such as those of the respiratory tract.

  4. More or less consistent with Zinkernagel’s data, even if memory cells were present CTL didn’t show a lot of presence in the spleen until the infection was well along. Virus levels in the spleen were already significantly reduced, compared to mice with no memory cells, well before any CTL were detectable in the spleen. But by this time, CTL activity was detectable in the draining lymph nodes. As far as I can remember, Zinkernagel didn’t check the lymph nodes for CTL activity.

Leonidas at ThermopylaeSo essentially, the model is that memory CTL do protect against virus infection because they’re present in the lymph nodes early enough, even though they’re not present in other sites. According to this model the draining lymph node is a bottleneck through which the virus must pass on its way from a local infection, to a systemic infection. By focusing attention to this critical pass, CTL can reduce (but not eliminate) the virus that goes systemic. By slowing down the infection, the rest of the immune system has time to catch up and stop the virus. (Image at the left is “Leonidas at Thermopylae, by Jacques-Louis David.) Without the CTL, the virus just explodes and there’s too much of it by the time the rest of the immune system tries to deal with it.

One caveat I have is that I’m not convinced that the draining lymph node is the critical spot. At least in this paper, it’s shown that CTL are active there, but not that CTL activity in the node is essential. It’s also possible that the virus spreads through some other route, and that other route is also blocked by CTL. In other words, I believe that Xu et al have shown a correlation between early CTL activity in draining lymph nodes and protection against ectromelia, but haven’t shown that this is causative. However, I do believe that the observation greatly weakens Zinkernagel’s argument.

Another question I have is how universal the truth is. It seems possible to me that the precise timing and rate of virus replication is critical. If you slow the replication with CTL, then the virus levels are low enough for B cells to deal with, but neither CTL nor B cells check the replication enough. How many viruses replicate just that fast, but no faster? Are there systemic viruses that replicate slowly enough that CTL could stop them altogether? Or some that replicate so fast, even CTL in the draining nodes can’t catch up? Is ectromelia unique, or is it a representative systemic virus?

The good news is that the techniques used here, while finicky, aren’t all that specialized: Most immunologists could reproduce the experiments using their favourite model. So we’re likely to learn where this is and is not applicable over the next few years.

  1. How often do you see such a simple logical train?[]
  2. Protective ‘immunity’ by pre-existent neutralizing antibody titers and preactivated T cells but not by so-called ‘immunological memory’. Rolf M. Zinkernagel, Hans Hengartner[]
  3. Do I need a disclaimer? Luis is a long-time friend of mine, but I had nothing to do with the work here[]
  4. Nature Reviews Microbiology 3, 201-213 (March 2005) | doi:10.1038/nrmicro1099. Poxvirus tropism. Grant McFadden[]
  5. The “parking period” was a 4 month period between transfer of memory CD8 cells, and challenge with the virus.[]